Search

Precision genomic run-on assays (PRO-seq) quantify nascent RNA at single nucleotide resolution with strand specificity. Here we deconstruct a recently published genomic nascent RNA processing pipeline (PEPPRO) into its components and link the analyses to the underlying molecular biology. PRO-seq experiments are evolving and variations can be found throughout the literature. The analyses are presented as individual code chunks with comprehensive details so that users can modify the framework to accommodate different protocols. We present the framework to quantify the following quality control metrics: library complexity, nascent RNA purity, nuclear run-on efficiency, alignment rate, sequencing depth, and RNA degradation.

Chromatin accessibility assays have revolutionized the field of transcription regulation by providing single-nucleotide resolution measurements of regulatory features such as promoters and transcription factor binding sites. ATAC-seq directly measures how well the Tn5 transpose accesses chromatinized DNA. Tn5 has a complex sequence bias that is not effectively scaled with traditional bias-correction methods. We model this complex bias using a rule ensemble machine learning approach that integrates information from many input k-mers proximal to the ATAC sequence reads. We effectively characterize and correct single-nucleotide sequence biases and regional sequence biases of the Tn5 enzyme. Correction of enzymatic sequence bias is an important step in interpreting chromatin accessibility assays that aim to infer transcription factor binding and regulatory activity of elements in the genome.

Inorganic scaling is a phenomenon of common occurrence both in nature and in industrial operations. In general, its effect can be highly detrimental for the oil industry, as fouling can take place in different stages of the production, from the well bore and downhole production control valves to upstream primary oil processing and separation equipment. The deposition of precipitated crystals on pipe walls and valves can result in severe production decline. Despite the high costs involved in the design and operation of separate lines for additive injection, chemical inhibition is typically the solution adopted by the oil companies to mitigate scaling. The purpose of the present work is to show results of a large scale laboratory pipe flow experiments to evaluate the performance of non-chemical solutions to mitigate and control calcium carbonate scaling. Magnetic, electromagnetic and ultrasound devices have been tested in a set up that simulates the mixing of two incompatible brine solutions that cause precipitation and deposition of calcium carbonate for a high Reynolds number pipe flow. The performance of the devices is evaluated from pressure drop measurements along the pipe, carbonate deposited mass on the pipe wall and reduction of pipe diameter. Additional results comprise evaluation of particle size distribution of the precipitated crystal, scanning electron microscopy, x-ray diffraction for identification of the crystalline structure and pH and conductivity. Results show that the magnetic field furnishes a beneficial effect, as it delays the time observed for the onset of flow restriction in both pipe and valve. The scaling phenomenon is shown to slow down and the delay for the increase in the measured pressure drop range between two to four times in comparison with the tests conducted without the magnetic field. Ultrasound devices are also shown to provide remarkable impact on the delay of the appearance of the scaling effects. The ultrasound field influences the precipitation phenomena so that particle sizes are kept at very small values, which prevent crystal deposition. The main contribution of the present work is to provide an evaluation method of anti-scaling devices based on large scale experiments, which are fairly representative of real field applications.

Topic modeling or inference has been one of the well-known problems in the area of text mining. It deals with the automatic categorisation of words or documents into similarity groups also known as topics. In most of the social media platforms such as Twitter, Instagram, and Facebook, hashtags are used to define the content of posts. Therefore, modelling of hashtags helps in categorising posts as well as analysing user preferences. In this work, we tried to address this problem involving hashtags that stream in real-time. Our approach encompasses graph of hashtags, dynamic sampling and modularity based community detection over the data from a popular social media engagement application. Further, we analysed the topic clusters’ structure and quality using empirical experiments. The results unveil latent semantic relations between hashtags and also show frequent hashtags in a cluster. Moreover, in this approach, the words in different languages are treated synonymously. Besides, we also observed top trending topics and correlated clusters.

How evolutionary changes at enhancers affect the transcription of target genes remains an important open question. Previous comparative studies of gene expression have largely measured the abundance of messenger RNA, which is affected by post-transcriptional regulatory processes, hence limiting inferences about the mechanisms underlying expression differences. Here, we directly measured nascent transcription in primate species, allowing us to separate transcription from post-transcriptional regulation. We used precision run-on and sequencing to map RNA polymerases in resting and activated CD4+ T cells in multiple human, chimpanzee and rhesus macaque individuals, with rodents as outgroups. We observed general conservation in coding and non-coding transcription, punctuated by numerous differences between species, particularly at distal enhancers and non-coding RNAs. Genes regulated by larger numbers of enhancers are more frequently transcribed at evolutionarily stable levels, despite reduced conservation at individual enhancers. Adaptive nucleotide substitutions are associated with lineage-specific transcription and at one locus, SGPP2, we predict and experimentally validate that multiple substitutions contribute to human-specific transcription. Collectively, our findings suggest a pervasive role for evolutionary compensation across ensembles of enhancers that jointly regulate target genes.

Modifications to the global run-on and sequencing (GRO-seq) protocol that enrich for 5'-capped RNAs can be used to reveal active transcriptional regulatory elements (TREs) with high accuracy. Here, we introduce discriminative regulatory-element detection from GRO-seq (dREG), a sensitive machine learning method that uses support vector regression to identify active TREs from GRO-seq data without requiring cap-based enrichment (https://github.com/Danko-Lab/dREG/). This approach allows TREs to be assayed together with gene expression levels and other transcriptional features in a single experiment. Predicted TREs are more enriched for several marks of transcriptional activation—including expression quantitative trait loci, disease-associated polymorphisms, acetylated histone 3 lysine 27 (H3K27ac) and transcription factor binding—than those identified by alternative functional assays. Using dREG, we surveyed TREs in eight human cell types and provide new insights into global patterns of TRE function.

Coupling molecular biology to high throughput sequencing has revolutionized the study of biology. Molecular genomics techniques are continually refined to provide higher resolution mapping of nucleic acid interactions and structure. Sequence preferences of enzymes can interfere with the accurate interpretation of these data. We developed seqOutBias to characterize enzymatic sequence bias from experimental data and scale individual sequence reads to correct intrinsic enzymatic sequence biases. SeqOutBias efficiently corrects DNase-seq, TACh-seq, ATAC-seq, MNase-seq, and PRO-seq data. We show that seqOutBias correction facilitates identification of true molecular signatures resulting from transcription factors and RNA polymerase interacting with DNA.

Despite the conventional distinction between them, promoters and enhancers share many features in mammals, including divergent transcription and similar modes of transcription factor binding. Here we examine the architecture of transcription initiation through comprehensive mapping of transcription start sites (TSSs) in human lymphoblastoid B cell (GM12878) and chronic myelogenous leukemic (K562) ENCODE Tier 1 cell lines. Using a nuclear run-on protocol called GRO-cap, which captures TSSs for both stable and unstable transcripts, we conduct detailed comparisons of thousands of promoters and enhancers in human cells. These analyses identify a common architecture of initiation, including tightly spaced (110 bp apart) divergent initiation, similar frequencies of core promoter sequence elements, highly positioned flanking nucleosomes and two modes of transcription factor binding. Post-initiation transcript stability provides a more fundamental distinction between promoters and enhancers than patterns of histone modification and association of transcription factors or co-activators. These results support a unified model of transcription initiation at promoters and enhancers.

Analyses of the 2009 H1N1 influenza pandemic and post-pandemic years showed high attack rates and severity among indigenous populations. This study presents the characteristics of the first documented influenza outbreak in indigenous peoples in Brazil, that occurred from 30th March to 14th April 2016 in a Guarani village in Southeast Region. Acute respiratory infections were prospectively investigated. The majority of the 73 cases were influenza-like illness (ILI) (63.0%) or severe acute respiratory infection (SARI) (20.5%). The ILI+SARI attack rate (35.9%) decreased with increasing age. There was a high influenza vaccination rate (86.3%), but no statistically significant difference in vaccination rates between severe and non-severe cases was seen (p = 0.334). Molecular analyses of 19.2% of the cases showed 100% positivity for influenza A(H1N1)pdm09 and/or hRSV. Influenza A(H1N1)pdm09 was included in the 6B.1 genetic group, a distinct cluster with 13 amino acid substitutions of A/California/07/2009-like. The hRSV were clustered in the BA-like genetic group. The early arrival of the influenza season overlapping usual hRSV season, the circulation of a drifted influenza virus not covered by vaccine and the high prevalence of risk factors for infection and severity in the village jointly can explain the high attack rate of ARI, even with a high rate of influenza vaccination. The results reinforce the importance of surveillance of respiratory viruses, timely vaccination and controlling risk factors for infection and severity of in the indigenous populations in order to preventing disease and related deaths, particularly in children.

RNA polymerase II (Pol II) transcribes hundreds of kilobases of DNA, limiting the production of mRNAs and lncRNAs. We used global run-on sequencing (GRO-seq) to measure the rates of transcription by Pol II following gene activation. Elongation rates vary as much as 4-fold at different genomic loci and in response to two distinct cellular signaling pathways (i.e., 17β-estradiol [E2] and TNF-α). The rates are slowest near the promoter and increase during the first ~15 kb transcribed. Gene body elongation rates correlate with Pol II density, resulting in systematically higher rates of transcript production at genes with higher Pol II density. Pol II dynamics following short inductions indicate that E2 stimulates gene expression by increasing Pol II initiation, whereas TNF-α reduces Pol II residence time at pause sites. Collectively, our results identify previously uncharacterized variation in the rate of transcription and highlight elongation as an important, variable, and regulated rate-limiting step during transcription.

Transcriptional regulatory changes have been shown to contribute to phenotypic differences between species, but many questions remain about how gene expression evolves. Here we report the first comparative study of nascent transcription in primates. We used PRO-seq to map actively transcribing RNA polymerases in resting and activated CD4+ T-cells in multiple human, chimpanzee, and rhesus macaque individuals, with rodents as outgroups. This approach allowed us to measure transcription separately from post-transcriptional processes. We observed general conservation in coding and non-coding transcription, punctuated by numerous differences between species, particularly at distal enhancers and non-coding RNAs. We found evidence that transcription factor binding sites are a primary determinant of transcriptional differences between species, that stabilizing selection maintains gene expression levels despite frequent changes at distal enhancers, and that adaptive substitutions have driven lineage-specific transcription. Finally, we found strong correlations between evolutionary rates and long-range chromatin interactions. These observations clarify the role of primary transcription in regulatory evolution.

Information produced by people usually has an implicit agreed-upon structure. However, this structure is not usually available to computer programs, where it could be used, for example, to aid in answering search queries. For example, when considering technical articles, one could ask for the occurrence of a keyword in a particular part of the article, such as the reference section. This implicit structure could be used, in the form of an ontology, to further the efforts of improving search in the semantic web. We propose a method to build ontologies encoding this structure information by the application of grammar inference techniques. This results in a semi-automatic approach to the inference of such ontologies. Our approach has two main components: (1) the inference of a grammatical description of the implicit structure of the supplied examples, and (2) the transformation of that description into an ontology. We present the application of the method to the inference of an ontology describing the structure of technical articles.

Previous studies have shown that GAGA Factor (GAF) is enriched on promoters with paused RNA Polymerase II (Pol II), but its genome-wide function and mechanism of action remain largely uncharacterized. We assayed the levels of transcriptionally-engaged polymerase using global run-on sequencing (GRO-seq) in control and GAF-RNAi Drosophila S2 cells and found promoter-proximal polymerase was significantly reduced on a large subset of paused promoters where GAF occupancy was reduced by knock down. These promoters show a dramatic increase in nucleosome occupancy upon GAF depletion. These results, in conjunction with previous studies showing that GAF directly interacts with nucleosome remodelers, strongly support a model where GAF directs nucleosome displacement at the promoter and thereby allows the entry Pol II to the promoter and pause sites. This action of GAF on nucleosomes is at least partially independent of paused Pol II because intergenic GAF binding sites with little or no Pol II also show GAF-dependent nucleosome displacement. In addition, the insulator factor BEAF, the BEAF-interacting protein Chriz, and the transcription factor M1BP are strikingly enriched on those GAF-associated genes where pausing is unaffected by knock down, suggesting insulators or the alternative promoter-associated factor M1BP protect a subset of GAF-bound paused genes from GAF knock-down effects. Thus, GAF binding at promoters can lead to the local displacement of nucleosomes, but this activity can be restricted or compensated for when insulator protein or M1BP complexes also reside at GAF bound promoters., Author Summary Transcriptional regulation is critical for proper gene expression in response to environmental changes and developmental programs. Eukaryotes have evolved multiple mechanisms by which transcription factors regulate transcription. One mechanism is the reorganization of chromatin to allow Pol II recruitment. Another is the release of promoter-proximal paused Pol II, where Pol II transcription that is halted 20–60 bases downstream of the transcription start site (TSS) is allowed to enter into productive elongation through the gene body. The Drosophila transcription factor GAF binds to genes that undergo pausing and interacts with nucleosome remodelers and the pausing factor NELF. Thus, GAF can regulate multiple points necessary for transcription, but its mechanistic role is not fully understood genome-wide. We depleted GAF from cells and examined the genome-wide changes in Pol II and nucleosome distributions across genes. We found that GAF depletion reduces polymerase density at genes where GAF binds just upstream of the TSS, and results in nucleosomes moving into the promoter region. Our results show that GAF is important for maintaining the promoter accessibility, allowing Pol II to be recruited to promoters and enter the pause sites downstream of the TSS. Thus, GAF is critical for providing the chromatin environment necessary for the proper control of gene expression.

Identification of the genomic regions that regulate transcription remains an important open problem. We have recently shown that global run-on and sequencing (GRO-seq) with enrichment for 5′-capped RNAs reveals patterns of divergent transcription that accurately mark active transcriptional regulatory elements (TREs), including enhancers and promoters. Here, we demonstrate that active TREs can be identified with comparable accuracy by applying sensitive machine-learning methods to standard GRO-seq and PRO-seq data, allowing TREs to be assayed together with transcription levels, elongation rates, and other transcriptional features, in a single experiment. Our method, called discriminative Regulatory Element detection from GRO-seq (dREG), summarizes GRO-seq read counts at multiple scales and uses support vector regression to predict active TREs. The predicted TREs are strongly enriched for marks associated with functional elements, including H3K27ac, transcription factor binding sites, eQTLs, and GWAS-associated SNPs. Using dREG, we survey TREs in eight cell types and provide new insights into global patterns of TRE assembly and function.

To gain insights into evolutionary forces that have shaped the history of Bornean and Sumatran populations of orang-utans, we compare patterns of variation across more than 11 million single nucleotide polymorphisms found by previous mitochondrial and autosomal genome sequencing of 10 wild-caught orang-utans. Our analysis of the mitochondrial data yields a far more ancient split time between the two populations (~3.4 million years ago) than estimates based on autosomal data (0.4 million years ago), suggesting a complex speciation process with moderate levels of primarily male migration. We find that the distribution of selection coefficients consistent with the observed frequency spectrum of autosomal non-synonymous polymorphisms in orang-utans is similar to the distribution in humans. Our analysis indicates that 35% of genes have evolved under detectable negative selection. Overall, our findings suggest that purifying natural selection, genetic drift, and a complex demographic history are the dominant drivers of genome evolution for the two orang-utan populations. Publisher PDF

5 páginas, 5 figuras, 2 tablas.-- This paper is distributed under the terms of the Creative Commons Attribution-Non-Commercial-Share Alike Licence, and is freely available to all readers atwww.nature.com/nature.-- et al., ‘Orang-utan’ is derived from a Malay term meaning ‘man of the forest’ and aptly describes the southeast Asian great apes native to Sumatra and Borneo. The orang-utan species, Pongo abelii (Sumatran) and Pongo pygmaeus (Bornean), are the most phylogenetically distant great apes from humans, thereby providing an informative perspective on hominid evolution. Here we present a Sumatran orang-utan draft genome assembly and short read sequence data from five Sumatran and five Bornean orang-utan genomes. Our analyses reveal that, compared to other primates, the orang-utan genome has many unique features. Structural evolution of the orang-utan genome has proceeded much more slowly than other great apes, evidenced by fewer rearrangements, less segmental duplication, a lower rate of gene family turnover and surprisingly quiescent Alu repeats, which have played a major role in restructuring other primate genomes. We also describe a primate polymorphic neocentromere, found in both Pongo species, emphasizing the gradual evolution of orang-utan genome structure. Orang-utans have extremely low energy usage for a eutherian mammal1, far lower than their hominid relatives. Adding their genome to the repertoire of sequenced primates illuminates new signals of positive selection in several pathways including glycolipid metabolism. From the population perspective, both Pongo species are deeply diverse; however, Sumatran individuals possess greater diversity than their Bornean counterparts, and more species-specific variation. Our estimate of Bornean/Sumatran speciation time, 400,000 years ago, is more recent than most previous studies and underscores the complexity of the orang-utan speciation process. Despite a smaller modern census population size, the Sumatran effective population size (Ne) expanded exponentially relative to the ancestral Ne after the split, while Bornean Ne declined over the same period. Overall, the resources and analyses presented here offer new opportunities in evolutionary genomics, insights into hominid biology, and an extensive database of variation for conservation efforts., The orang-utan genome project was funded by the National Human Genome Research Institute (NHGRI), including grantsU54HG003079 (R.K.W.) and U54 HG003273 (R.A.G), with further support from National Institutes of Health R01 GM59290 (M.A.B.), PO1 AG022064 (M.A.B.), HG002385 (E.E.E.) and HG002238 (W.M.), National Science Foundation DBI-0644111 (A.S. and B.B.), David and Lucile Packard Foundation(A.S., V.T. and T.V.),CornellUniversity Provost’s Fellowship (A.L.M.), UK Medical Research Council (C.P.P., G.L., S.M. and A.H.), Marie Curie Fellowship (T.M.-B.), Ministerio de Ciencia e Innovación-Spain (MCI-Spain) and Fundación M. Botín (V.Q., X.S.P., G.R.O. and C.L.-O.), MCI-Spain BFU2006-15413-C02-01 and BFU2009-13409-C02-02 (A.N.), Spanish National Institute for Bioinformatics (INAB) and Fundação para a Ciência e a Tecnologia (Portugal), SFRH/BPD/26384/2006(R.F.) and SFRH/BD/15856/2005 (O.F.), PRIN and CEGBA (M.R., N.A. and G.D.V.), and the Commission of the European Communities IRG-224885 (T.V.), IRG-231025 (B.B.).

Despite active work during the past ten years, Ontology Engineering still lacks standard construction methodologies. The few existing methodologies do not include risk management to predict and control the risks that emerge from the many constraints involved in the construction process. Risk management techniques can smooth the problems faced in these complex construction processes. In this paper, an ontology construction process is described, to which a risk analysis process was adapted. These results represent a step to help newcomers in ontology engineering, pinpointing common risks, and their respective triggering events and effects.

In this work we propose to use a more powerful teacher to effectively apply query learning algorithms to identify regular languages in practical, real-world problems. More specifically, we define a more powerful set of replies to the membership queries posed by the L* algorithm that reduces the number of such queries by several orders of magnitude in a practical application. The basic idea is to avoid the needless repetition of membership queries in cases where the reply will be negative as long as a particular condition is met by the string in the membership query. We present an example of the application of this method to a real problem, that of inferring a grammar for the structure of technical articles.